Abstract P6-04-05: A fully automatic artificial intelligence system for accurate and reproducible HER2 IHC scoring in breast cancer
Authors: Yuval Globerson, Lilach Bien, Jonathan Harel, Giuseppe Mallel, Geraldine Sebag, Michel Vandenberghe, Craig Barker, Tsuyoshi Matsuo, Charo Garrido, Judith Sandbank, Chaim Linhart
Journal: Cancer Research (2023)

Abstract Objective: Tumor HER2 expression is a key prognostic and treatment influencing factor in breast cancer. As with all immunohistochemistry (IHC) staining, visual interpretation of HER2 expression is subjective, which leads to intra- and inter-pathologist variability. Recent findings on the efficacy of HER2-targeted therapy on HER2-low patients raise the need for accurate and reproducible scoring. We developed a fully automated, artificial intelligence (AI) -based algorithm for HER2 scoring. The algorithm was based on ASCO/CAP 2018 guidelines and validated against rigorous ground truth (GT) established by multiple blinded expert pathologists. Methods: Algorithm development: We developed a solution that employs two steps: The first step consists of an ensemble of Deep Learning networks that process tissue regions and classify them as various tissue classes: Invasive cancer, Ductal Carcinoma In Situ (DCIS) and other morphologies. These networks were trained on slides that were automatically labeled by a separate AI system that analyzed the corresponding H&amp;E slides and projected its findings to the HER2 IHC slides using a registration algorithm. To further enrich the training set, especially with rare and difficult cases, a team of 8 expert pathologists manually marked tissue areas and assigned them to one of the tissue classes. In total, the training set consisted of 6,400 manual annotations and 1,300 automatically-annotated slides, both collected from 9 laboratories and scanned using 3 different scanners. The second step is an ensemble of Object Detection networks that process only the regions classified as invasive cancer, detect the tumor cells within them, and classify their staining pattern (e.g., Not stained, Moderate incomplete, etc.). Finally, the detected cells are counted, and the ASCO/CAP guidelines are applied to derive the slide-level HER2 score. Validation: The validation set was comprised of 453 HER2 slides stained using the VENTANA anti-HER2/neu (4B5) Rabbit Monoclonal Primary Antibody as per manufacturer’s instructions. HER2 slides included biopsies and excisions with different breast cancer diagnoses (e.g., Infiltrating Ductal Carcinoma (IDC), Infiltrating Lobular Carcinoma (ILC), rare invasive subtypes, with and without DCIS) from 3 different laboratories. Ground truth was established by the consensus scores of a panel of 3 pathologists, who scored HER2 according to the guidelines without additional clinical considerations, such as scoring borderline 1+/2+ cases as 2+ to have additional tests performed. Results: The algorithm showed very high performance for detecting invasive cancer in HER2 tissue sections, with AUC of 0.967 (measured on 4-fold Cross-Validation classifying invasive vs. other regional classes). The algorithm demonstrated an overall accuracy of 80.3% for the HER2 scores when compared to the GT. When using different cutoffs for binary classification the resulting performance was: for 0 vs 1+/2+/3+ Kappa was 0.800; 0/1+ vs 2+/3+ Kappa was 0.728; for 0/1+/2+ vs 3+ Kappa was 0.954. The Quadratic Kappa between the AI score and the GT was 0.898, which is considered almost perfect. The performance of the AI was similar across the different laboratories and diagnoses(e.g. IDC, ILC). Conclusion: This study reports the successful development and independent validation of a fully automatic AI-based solution for accurate HER2 scoring in breast cancer. AI solutions, such as the one reported here, could be used as decision-support tools for pathologists in routine clinical practice, enhancing the reproducibility and consistency of HER2 scoring, thus enabling optimal treatment pathways and better patient outcomes. Accurate and automatic IHC scoring solutions can also contribute to the development of new prognostic, predictive and companion diagnostic tools. Citation Format: Yuval Globerson, Lilach Bien, Jonathan Harel, Giuseppe Mallel, Geraldine Sebag, Michel Vandenberghe, Craig Barker, Tsuyoshi Matsuo, Charo Garrido, Judith Sandbank, Chaim Linhart. A fully automatic artificial intelligence system for accurate and reproducible HER2 IHC scoring in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-05.